General Data Research Articles

Trends and risk factors analysis of NEC in preterm infants over 9 years

BackgroundInvestigating the trend of changes in the occurrence of necrotizing enterocolitis (NEC) in preterm infants during 9 years and analyzing the risk factors of NEC with the purpose of providing reference for clinical diagnosis and treatment of NEC.MethodsClinical data of NEC in preterm infants with Bell’s stage ≥ II from January 2013 to December 2021 in the Neonatology Department of the Third Affiliated Hospital of Zunyi Medical University was retrospectively analyzed. Trends in the occurrence of NEC in preterm infants were analyzed by the trend chi-square test. Subsequently, the general data (sex, gestational age, singleton or multiple births, birth weight, serum albumin, alkaline phosphatase, sepsis, blood transfusion, mechanical ventilation, RDS, arterial catheterization) and perinatal data (intrauterine distress, turbid amniotic fluid, premature rupture of membranes, mode of delivery, fetal heart abnormalities, diabetes mellitus) were collected; then, the risk factors for NEC were analyzed by univariate and multivariate logistic-regression analysis.ResultsIn the past 9 years, 77 cases of NEC occurred, with the incidence rate of 1.95%, and the incidence of NEC in preterm infants has been increasing year by year (P < 0.05). The results of univariate analysis showed that the morbidity of NEC in preterm infants was associated with premature rupture of membranes, blood transfusion, sepsis, and the of serum albumin (P < 0.05). Multivariate logistic regression analysis revealed that blood transfusion (OR = 2.232, 95% CI: 1.012–4.923) and sepsis (OR = 0.899, 95% CI: 0.809–3.915) were independent risk factors of NEC in preterm infants, while high serum albumin (OR = 0.899, 95% CI: 0.809–3.915) was an independent protective factor of NEC in preterm infants.ConclusionThe morbidity of NEC is gradually increasing. Inhibition of infection and limitation of blood transfusion are effective measures to reduce the occurrence of NEC. Meanwhile, high serum albumin is a protective factor for NEC.

Clinical Results of Asymmetric Blepharoplasty Plastic Repair.

To investigate plastic surgery repair's effects and adverse reactions in the clinical therapy of asymmetric double eyelids. All 126 sufferers who came to the hospital for asymmetric double eyelid plastic repair from January 2022 to October 2022 were selected as the research objects, and they were divided into a control group and an observation group using the random number method, with 63 cases in each group, in which sufferers in the control group underwent full incision blepharoplasty and sufferers in the observation group underwent small incision liposuction with submerged sutures. The general data, treatment results, treatment satisfaction, related surgical indicators, and frequency of adverse reactions of the 2 groups of sufferers with asymmetric double eyelid plastic repair were compared. It had no statistically obvious distinction between the control group and the observation group of sufferers in terms of general data such as sex, age, weight, and height (P>0.05); the total therapy efficiency of the sufferers in the observation group (95.24%) was greater than the control group (74.60%), with P value <0.05; the total treatment satisfaction of the patients in the observation group (96.83%) was significantly higher than that in the control group (76.19%), with P value <0.05; compared with the control group, patients in the observation group had shorter operative time and healing time and less intraoperative bleeding, with P value <0.05; the total frequency of adverse reactions of sufferers in the observation group (4.76%) was less than the control group (17.46%), with P value <0.05. Small incision liposuction with submerged sutures for plastic repair has significant efficacy, relatively high patient satisfaction, and low incidence of adverse reactions, in line with patient esthetic review, which has a broad clinical application prospect.

Shape completion in the dark: completing vertebrae morphology from 3D ultrasound.

Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information. While performing US-based diagnosis or investigation, medical professionals therefore create a mental map of the 3D anatomy. In this work, we aim to replicate this process and enhance the visual representation of anatomical structures. We introduce a point cloud-based probabilistic deep learning (DL) method to complete occluded anatomical structures through 3D shape completion and choose US-based spine examinations as our application. To enable training, we generate synthetic 3D representations of partially occluded spinal views by mimicking US physics and accounting for inherent artifacts. The proposed model performs consistently on synthetic and patient data, with mean and median differences of 2.02 and 0.03 in Chamfer Distance (CD), respectively. Our ablation study demonstrates the importance of US physics-based data generation, reflected in the large mean and median difference of 11.8 CD and 9.55 CD, respectively. Additionally, we demonstrate that anatomical landmarks, such as the spinous process (with reconstruction CD of 4.73) and the facet joints (mean distance to ground truth (GT) of 4.96 mm), are preserved in the 3D completion. Our work establishes the feasibility of 3D shape completion for lumbar vertebrae, ensuring the preservation of level-wise characteristics and successful generalization from synthetic to real data. The incorporation of US physics contributes to more accurate patient data completions. Notably, our method preserves essential anatomical landmarks and reconstructs crucial injections sites at their correct locations.

The relationship between serum coagulation parameters and the recurrence of chronic subdural hematoma.

The aim is to investigate the relationship between serum coagulation parameters (PT, APTT, D-D and FDP) before hospitalization and recurrence of chronic subdural hematoma (CSDH). 236 patients with CSDH who were diagnosed for the first time and had complete medical records were followed up for at least 90days. Fifty patients (21.2%) had relapsed. Univariate analysis was conducted including general data, imaging data and test results. Serum coagulation parameters (PT, APTT, D-D and FDP) were detected for all CSDH patients. The study identified several factors that exhibited a significant correlation with chronic subdural hematoma (CSDH) recurrence. These factors included advanced age (p = 0.01), hypertension (p = 0.04), liver disease (p = 0.01), anticoagulant drug use (p = 0.01), antiplatelet drug use (p = 0.02), bilateral hematoma (p = 0.02), and single-layer hematoma (p = 0.01). In addition, the presence of fibrin/fibrinogen degradation products (FDP) exceeding 5mg/L demonstrated a significant relationship with CSDH recurrence (P < 0.05). Notably, the combined assessment of D-dimer (D-D) and FDP exhibited a significant difference, particularly regarding recurrence within 30days after surgery (P < 0.05). The simultaneous elevation of serum FDP and D-D levels upon admission represents a potentially novel predictor for CSDH recurrence. This finding is particularly relevant for patients who experience recurrence within 30days following surgical intervention. Older individuals with CSDH who undergo trepanation and drainage should be closely monitored due to their relatively higher recurrence rate.

Views of general practice staff on sharing general practice data for research: a scoping review protocol.

The objective of this scoping review is to understand the range and types of evidence in relation to the views of general practitioner and other general practice staff on sharing general practice data for research purposes. The use of general practice data for research has the potential to drive transformative improvements in health care. The vast amount of patient data collected in general practice is valuable and provides researchers with data to conduct large-scale studies and generate evidence that can inform policy decisions, support the development of personalized medicine, and enhance patient outcomes. However, despite there being clear benefits to using general practice data for research, there are also potential harms, such as data misuse, loss of trust between the general practitioner and patient, and data breaches. This scoping review will focus on the views of general practice staff, including general practitioners, practice nurses, and practice managers, about sharing general practice data for the purposes of research. This scoping review will exclude sources of evidence that are conducted outside of the general practice setting, and papers that report on public, patient, or community views on data sharing. This scoping review will be conducted in accordance with JBI methodology for scoping reviews. A 3-step search strategy will be used to acquire both published and unpublished sources of evidence. Two independent reviewers will select sources of evidence in line with the inclusion and exclusion criteria. No limits on the date of the search or language will be applied. Data will be extracted and the results will be summarized descriptively and presented in a tabular format. Open Science Framework https://osf.io/49yw5.

Enhancing Workplace Safety through Personalized Environmental Risk Assessment: An AI-Driven Approach in Industry 5.0

This paper describes an integrated monitoring system designed for individualized environmental risk assessment and management in the workplace. The system incorporates monitoring devices that measure dust, noise, ultraviolet radiation, illuminance, temperature, humidity, and flammable gases. Comprising monitoring devices, a server-based web application for employers, and a mobile application for workers, the system integrates the registration of workers’ health histories, such as common diseases and symptoms related to the monitored agents, and a web-based recommendation system. The recommendation system application uses classifiers to decide the risk/no risk per sensor and crosses this information with fixed rules to define recommendations. The system generates actionable alerts for companies to improve decision-making regarding professional activities and long-term safety planning by analyzing health information through fixed rules and exposure data through machine learning algorithms. As the system must handle sensitive data, data privacy is addressed in communication and data storage. The study provides test results that evaluate the performance of different machine learning models in building an effective recommendation system. Since it was not possible to find public datasets with all the sensor data needed to train artificial intelligence models, it was necessary to build a data generator for this work. By proposing an approach that focuses on individualized environmental risk assessment and management, considering workers’ health histories, this work is expected to contribute to enhancing occupational safety through computational technologies in the Industry 5.0 approach.

A Causality-Aware Perspective on Domain Generalization via Domain Intervention

Most mainstream statistical models will achieve poor performance in Out-Of-Distribution (OOD) generalization. This is because these models tend to learn the spurious correlation between data and will collapse when the domain shift exists. If we want artificial intelligence (AI) to make great strides in real life, the current focus needs to be shifted to the OOD problem of deep learning models to explore the generalization ability under unknown environments. Domain generalization (DG) focusing on OOD generalization is proposed, which is able to transfer the knowledge extracted from multiple source domains to the unseen target domain. We are inspired by intuitive thinking about human intelligence relying on causality. Unlike relying on plain probability correlations, we apply a novel causal perspective to DG, which can improve the OOD generalization ability of the trained model by mining the invariant causal mechanism. Firstly, we construct the inclusive causal graph for most DG tasks through stepwise causal analysis based on the data generation process in the natural environment and introduce the reasonable Structural Causal Model (SCM). Secondly, based on counterfactual inference, causal semantic representation learning with domain intervention (CSRDN) is proposed to train a robust model. In this regard, we generate counterfactual representations for different domain interventions, which can help the model learn causal semantics and develop generalization capacity. At the same time, we seek the Pareto optimal solution in the optimization process based on the loss function to obtain a more advanced training model. Extensive experimental results of Rotated MNIST and PACS as well as VLCS datasets verify the effectiveness of the proposed CSRDN. The proposed method can integrate causal inference into domain generalization by enhancing interpretability and applicability and brings a boost to challenging OOD generalization problems.

The right to be forgotten: separate conceptual issues

The article is devoted to the study of the emergence and formation of the human right to be forgotten as the right of an individual to request the removal of his or her personal data from public access through search engines, under certain conditions. It is found that the establishment of the right to be forgotten is often associated with one of the key decisions of the Court of Justice of the European Union, namely, in the case of Google Spain v. Costeja Gonzalez, which has gained wide international publicity. However, it is important to note that freedom of speech and freedom of access to information are conflicting fundamental rights to the right to be forgotten. The right to privacy and the data protection regime do not exist in a vacuum: the right to privacy can often clash with other rights, in particular the right to freedom of expression, for example, in terms of censorship. The author analyses international judicial precedents regarding the acknowledgement of the right to be forgotten in the sphere of the digital human rights system, and finds that the rulings typically pertain to data that might drastically influence the fundamental rights to privacy and personal data protection. Furthermore, the courts explicitly assert their intention to achieve an equitable balance between these rights and the public's right to access information. Hence, the resolution of numerous conflicts is largely contingent on the factual context of each case. The author reviews the legal regulation of the right to be forgotten, in particular, the Regulation of the European Parliament and of the Council of 27 April 2016 (General Data Protection Regulation), the 1995 Data Protection Directive, as updated and supplemented by the above-mentioned Regulation, and the European Convention on Human Rights, etc. Although the concept of the right to be forgotten is not recognised in the Ukrainian legal system, the Law of Ukraine "On Personal Data Protection” states that deletion or destruction of personal data in the information landscape is possible upon filing a complaint or going to court.

Laparoscopic-assisted distal colon excision and proximal colon pull-through anorectoplasty for anorectal malformation

PurposeDuring the second stage surgery for anorectal malformations (ARM), patients whose distal intestine of the colostomy is particularly short underwent laparoscopic-assisted distal colon excision and proximal colon pull-through anorectoplasty (PCPARP). This study aimed to discuss the outcomes of PCPARP after colostomy in patients with ARM.MethodsThis is a single-center propensity score-matched (PSM) study which was retrospectively initiated patients with intermediate- or high-type ARM who underwent laparoscopic surgery from June 2007 to December 2018. These patients were divided into PCPARP group and conventional laparoscopic-assisted anorectoplasty (LAARP) group according to specific surgical methods. The general data, surgical data, postoperative complications, and functional results were evaluated.ResultsIn total, 216 patients were included in this study: 190 (88.0%) undergoing LAARP approach and 26 (12.0%) undergoing PCPARP approach. After PSM, two well-balanced groups of 26 patients were analyzed and showed the postoperative complications (P = 0.126) and bowel function (P = 0.809) were similiar between the two groups.ConclusionsThe curative effect of PCPARP after colostomy is similar to that of classic LAARP surgery, which can be used for ARM patients with a very short and abnormal distal intestine of the stoma.

Psychometric Properties of the Spanish Version of the Cognitive Problems and Strategies Assessment in Patients with Bipolar Disorder.

Cognitive deficits play an important role in Bipolar Disorder (BPD). The Cognitive Problems and Strategies Assessment (CPSA) is a measure that evaluates the patient's perception of cognitive difficulties, and the spontaneous use of compensatory strategies and could thus have potential utility for clinical practice in patients with BPD. Our aim was to determine the validity and reliability of the Cognitive Problems and Strategies Assessment (CPSA) in Bipolar Disorder (BPD). Ninety-three BPD outpatients and 90 controls completed the Assessment of Problems with Thinking and Memory (APTM) questionnaire and the Assessment of Memory and Thinking Strategies (AMTS) questionnaire which constitute the CPSA, the Cognitive Complaints in Bipolar Disorder Rating Assessment (COBRA), as a measure of convergent validity, and general sociodemographic data. Cronbach's alpha coefficient, Spearman's correlation coefficient and independent sample t tests were used for Internal consistency, Convergent validity and Discriminant validity. The APTM had a Cronbach's alpha coefficient of 0.93 and the AMTS 0.90. The COBRA score and the APTM were significantly correlated. BPD patients exhibited higher scores on the APTM and lower scores on the AMTS than controls. The present instrument enriches the clinician's repertoire for rapid and inexpensive cognitive evaluation in BPD.

Risk Factors for Delayed Atrioventricular Block after Aortic Valve Surgery: A Retrospective Study

Objective: This study aimed to examine the potential factors that contribute to the occurrence of delayed high-grade atrioventricular block (DHAVB) following transcatheter aortic valve replacement (TAVR). Methods: A retrospective analysis was conducted on the clinical data of 115 patients who underwent TAVR at Jiaozhou Central Hospital of Qingdao Hospital between January 2018, and June 2023. A follow-up period of 30 days post-operation was observed for all patients. The patients were categorized into two groups on the basis of the occurrence of DHAVB: DHAVB group (n = 35) and control group (n = 80). The general clinical data preoperative and postoperative heart disease characteristics of the groups were compared. The risk factors associated with DHAVB after TAVR were analyzed. Results: The mean systolic blood pressure (SBP) level of the DHAVB group significantly increased compared with that of the control group, whereas the heart rate (HR) level significantly reduced (p &lt; 0.05). The average preoperative left ventricular ejection fraction (LVEF) was significantly lower in the DHAVB group than in the control group (p &lt; 0.05). The control group exhibited a significantly higher prevalence of preoperative QRS wave broadening, severe calcification of the aortic valve, and right bundle branch block than the control group (p &lt; 0.05). Spearman's correlation and logistic regression analyses identified increased SBP, decreased HR, diminished LVEF, the presence of preoperative and postoperative right bundle branch block, and thickened interventricular septum were as risk factors for DHAVB in patients undergoing TAVR (p &lt; 0.05). Conclusion: Close surveillance of blood pressure, heart rate, and cardiac function is recommended for individuals undergoing TAVR. Pre-operative and post-operative electrocardiography and echocardiography are valuable tools in identifying potential risk factors for DHAVB, offering a solid foundation for effective patient prognostic management.

Suture passer combined with two-hole laparoscopic peritoneal dialysis catheterization in patients undergoing peritoneal dialysis

Background Laparoscopic techniques are being widely applied for peritoneal dialysis (PD) catheter (PDC) placement. The suture passer is a novel fixation tool that aims to reduce catheter migration. We compared the clinical value of the suture passer combined with two-hole laparoscopic PDC placement to open surgical placement by evaluating preoperative and postoperative conditions, as well as the onset of complications in both groups. Methods A retrospective study was conducted including 169 patients who underwent PDC placement surgery from January 2021 to May 2023. Based on the method employed, patients were divided into two groups: the suture passer combined with a two-hole laparoscopy group (SLG) and the open surgical group (SG). Comprehensive patient information, including general data, preoperative and postoperative indicators, peritoneal function after surgery, and the incidence rate of complications, were collected and analyzed. Results The SLG showed a statistically significant decrease in operative time, intraoperative blood loss, and 6-month postoperative drift rate compared to the SG (p < 0.05). No statistically significant differences were observed between the two groups in terms of sex, age, primary disease, hospitalization time, hospitalization costs, preoperative and postoperative examination indicators, peritonitis, and omental wrapping. Conclusions Suture passer combined with two-hole laparoscopic PDC placement, characterized by simplicity and facilitating secure catheter fixation, was deemed safe and effective for patients undergoing PD. It reduces the catheter migration rate and improved surgical comfort. Overall, this technique demonstrates favorable outcomes in clinical practice.

Cybersecurity of Internet of Things in the health sector: Understanding the applicable legal framework

Although the digitalisation of healthcare is an ongoing process that dates back to more than two decades ago, it has gained more momentum with the COVID-19 pandemic. Recent developments in this sector include the adoption of wearable devices based on Internet of Things technology. The possibility of connecting devices that can work outside the physical boundaries of a hospital and follow patients at home, i.e. during their day-to-day life, has several obvious advantages. However, the digitalisation of the health sector through increased adoption of connected devices does not exclude vulnerabilities, particularly risks concerning the protection of patients’ data and the security of networks and information systems. Connected devices can gather, process, and store personal patient health data. Failure to safeguard the integrity and security of these data may affect the patients’ identity and finances and put their lives at risk. The presence of an IoT device in a healthcare setting may affect and reduce the level of network security of the overall system as it may provide an access point for an unlawful hacking attack. Although IoT technologies in the health sector are becoming increasingly pervasive, the European legal framework applicable to them is not clearly defined. This is extremely relevant in the case of cybersecurity, where the legal point of reference is the General Data Protection Regulation, addressing the measures and requirements applicable in case of data breaches, and the Medical Device Regulation, providing provisions focused on the security of data relevant to IoT defined as medical devices. The most recent interventions that address health data processing and cybersecurity are the proposed Cyber Resilience Act and the Health Data Space Regulation. The two acts provide measures and requirements applicable to IoT from two perspectives. Yet, they add complexities and some inconsistencies that may hamper the effectiveness of the overall cybersecurity framework.

Predicting unplanned hospitalisations in older adults using routinely recorded general practice data.

Unplanned hospitalisations represent a hazardous event for older persons. Timely identification of high-risk individuals using a prediction tool may facilitate preventive interventions. To develop and validate an easy-to-use prediction model for unplanned hospitalisations in community-dwelling older adults using readily available data to allow rapid bedside assessment by general practitioners. Retrospective study using general practice electronic health records of 243,129 community-dwelling adults aged ≥65 years linked with national administrative data. The dataset was geographically split into a development (58.7%) and validation (41.3%) sample to predict unplanned hospitalisations within 6 months. We evaluated the performance of three different models with increasingly smaller selections of candidate predictors (i.e. optimal, readily-available and easy-to-use model, respectively). We used logistic regression with backward selection for model development. The models were validated internally and externally. We assessed predictive performance by area under the curve (AUC) and calibration plots. In both samples, 7.6% had at least one unplanned hospitalisation within 6 months. The discriminative ability of the three models was comparable and remained stable after geographic validation. The easy-to-use model included age, sex, prior hospitalisations, pulmonary emphysema, heart failure and polypharmacy. Its discriminative ability after validation was AUC 0.72 [95% confidence interval: 0.72-0.71]. Calibration plots showed good calibration. Our models showed satisfactory predictive ability. Reducing the number of predictors and geographic validation did not impact predictive performance, demonstrating the robustness of the model. We developed an easy-to-use tool that may assist general practitioners in decision-making and targeted preventive interventions.

Bio2Py: An API for integrating Python with BioWin for enhanced data acquisition in wastewater treatment simulations

Commercial simulators for modeling wastewater treatment processes (WWTPs), such as BioWin, play a crucial role for optimizing the design, operation, and control of treatment facilities. These simulators offer fast and user-friendly interfaces and can be employed to enhance the efficiency of wastewater treatment plants. However, a notable limitation of BioWin and similar platforms is the lack of direct integration with Python®, a programming language renowned for its versatility and extensive data analysis and machine learning libraries. Bridging this gap constitutes an opportunity to significantly improve the simulation process by leveraging Python's capabilities. The Python package Bio2Py (BioWin to Python) is presented as a compelling solution, reconciling BioWin's specialized functionality and Python's automation and data processing strengths. By utilizing Python's PyAutoGUI package, Bio2Py automates various aspects of the simulation process, such as loading influent data, running simulations, and saving simulation results without manual intervention. The integration of BioWin with Python not only speeds up the workflow by automating repetitive tasks but also opens new possibilities, such as parameter calibration for specific wastewaters and using BioWin as a data generator for machine learning applications. This brief communication presents the Bio2Py API, illustrating its use and applicability through a simple implementation example. Bio2Py's ability to automate and optimize various aspects of wastewater treatment process simulation, including data acquisition, and its potential use for process variable optimization and parameter tuning, positions it as a valuable tool for engineers and researchers who work in this field.

Sustainable edge computing: Challenges and future directions

AbstractThe advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

Synthetic Data for Deep Learning in Computer Vision &amp; Medical Imaging: A Means to Reduce Data Bias

Deep-learning (DL) performs well in computer-vision and medical-imaging automated decision-making applications. A bottleneck of DL stems from the large amount of labelled data required to train accurate models that generalise well. Data scarcity and imbalance are common problems in imaging applications that can lead DL models towards biased decision making. A solution to this problem is synthetic data. Synthetic data is an inexpensive substitute to real data for improved accuracy and generalisability of DL models. This survey reviews the recent methods published in relation to the creation and use of synthetic data for computer-vision and medical-imaging DL applications. The focus will be on applications that utilised synthetic data to improve DL models by either incorporating an increased diversity of data that is difficult to obtain in real life, or by reducing a bias caused by class imbalance. Computer-graphics software and generative networks are the most popular data generation techniques encountered in the literature. We highlight their suitability for typical computer-vision and medical-imaging applications, and present promising avenues for research to overcome their computational and theoretical limitations.

Bacterial enrichment prior to third-generation metagenomic sequencing improves detection of BRD pathogens and genetic determinants of antimicrobial resistance in feedlot cattle

IntroductionBovine respiratory disease (BRD) is one of the most important animal health problems in the beef industry. While bacterial culture and antimicrobial susceptibility testing have been used for diagnostic testing, the common practice of examining one isolate per species does not fully reflect the bacterial population in the sample. In contrast, a recent study with metagenomic sequencing of nasal swabs from feedlot cattle is promising in terms of bacterial pathogen identification and detection of antimicrobial resistance genes (ARGs). However, the sensitivity of metagenomic sequencing was impeded by the high proportion of host biomass in the nasal swab samples.MethodsThis pilot study employed a non-selective bacterial enrichment step before nucleic acid extraction to increase the relative proportion of bacterial DNA for sequencing.ResultsNon-selective bacterial enrichment increased the proportion of bacteria relative to host sequence data, allowing increased detection of BRD pathogens compared with unenriched samples. This process also allowed for enhanced detection of ARGs with species-level resolution, including detection of ARGs for bacterial species of interest that were not targeted for culture and susceptibility testing. The long-read sequencing approach enabled ARG detection on individual bacterial reads without the need for assembly. Metagenomics following non-selective bacterial enrichment resulted in substantial agreement for four of six comparisons with culture for respiratory bacteria and substantial or better correlation with qPCR. Comparison between isolate susceptibility results and detection of ARGs was best for macrolide ARGs in Mannheimia haemolytica reads but was also substantial for sulfonamide ARGs within M. haemolytica and Pasteurella multocida reads and tetracycline ARGs in Histophilus somni reads.DiscussionBy increasing the proportion of bacterial DNA relative to host DNA through non-selective enrichment, we demonstrated a corresponding increase in the proportion of sequencing data identifying BRD-associated pathogens and ARGs in deep nasopharyngeal swabs from feedlot cattle using long-read metagenomic sequencing. This method shows promise as a detection strategy for BRD pathogens and ARGs and strikes a balance between processing time, input costs, and generation of on-target data. This approach could serve as a valuable tool to inform antimicrobial management for BRD and support antimicrobial stewardship.

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

Association between emergency department attendances, sociodemographic factors and long-term health conditions in the population of Norfolk and Waveney, England: Cross sectional study.

Demand for urgent and emergency health care in England has grown over the last decade, for reasons that are not clear. Changes in population demographics may be a cause. This study investigated associations between individuals' characteristics (including socioeconomic deprivation and long term health conditions (LTC)) and the frequency of emergency department (ED) attendances, in the Norfolk and Waveney subregion of the East of England. The study population was people who were registered with 91 of 106 Norfolk and Waveney general practices during one year from 1 April 2022 to 31 March 2023. Linked primary and secondary care and geographical data included each individual's sociodemographic characteristics, and number of ED attendances during the same year and, for some individuals, LTCs and number of general practice (GP) appointments. Associations between these factors and ED attendances were estimated using Poisson regression models. 1,027,422 individuals were included of whom 57.4% had GP data on the presence or absence of LTC, and 43.1% had both LTC and general practitioner appointment data. In the total population ED attendances were more frequent in individuals aged under five years, (adjusted Incidence Rate Ratio (IRR) 1.25, 95% confidence interval 1.23 to 1.28) compared to 15-35 years); living in more socioeconomically deprived areas (IRR 0.61 (0.60 to 0.63)) for least deprived compared to most deprived,and living closer to the nearest ED. Among individuals with LTC data, each additional LTC was also associated with increased ED attendances (IRR 1.16 (1.15 to 1.16)). Among individuals with LTC and GP appointment data, each additional GP appointment was also associated with increased ED attendances (IRR 1.03 (1.026 to 1.027)). In the Norfolk and Waveney population, ED attendance rates were higher for young children and individuals living in more deprived areas and closer to EDs. In individuals with LTC and GP appointment data, both factors were also associated with higher ED attendance.